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langfuse-mcp-java

get_dataset_run

get_dataset_run
Destructive

Retrieve a specific dataset run with all associated items, linking dataset entries to traces and observations for analysis of LLM application performance.

Instructions

Returns a single dataset run including all its run items. Each run item links a dataset item to a trace and optional observation. Returns: id, name, datasetName, metadata, createdAt, updatedAt, datasetRunItems[]. Both datasetName and runName are required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
datasetNameYesDataset name (exact match). Required.
runNameYesRun name (exact match). Required.
Behavior1/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

CRITICAL CONTRADICTION: The description frames this as a safe retrieval ('Returns a single...') but annotations indicate destructiveHint=true and readOnlyHint=false, implying data mutation or deletion. The description completely fails to explain why a 'get' operation is destructive, what gets destroyed, or any side effects.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Efficient 4-sentence structure that front-loads the action, explains run item semantics, and documents return fields (necessary given no output schema). Only minor redundancy in stating required parameters.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequately compensates for missing output schema by enumerating return fields and nested array structure (datasetRunItems). Would benefit from explaining the destructive behavior implied by annotations, but covers the data structure well.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description redundantly states parameters are 'required' (already in schema's required array) and doesn't add validation rules, format examples, or semantic context beyond the schema's 'exact match' specification.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it retrieves a single dataset run and explains what run items are (links between dataset items and traces). However, the framing as a simple 'Returns' operation slightly undersells the complexity suggested by the destructive annotation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Lacks explicit guidance on when to use this versus the sibling 'list_dataset_runs' tool. While 'single' implies random-access retrieval, it doesn't clarify selection criteria or when listing is preferable.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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